Natural language understanding is one component of digital assistants and other dialog systems. The natural language understanding component uses machine learning models to extract semantic meaning from input into the system.
Training a machine learning model in a natural language understanding component is typically accomplished at startup by using synthetic data created by a developer or other entity. However, such synthetic data is inevitably mismatched to actual user input which causes relatively lower semantic meaning recognition until sufficient data is collected and the model retrained. Collecting sufficient data can take a relatively long period of time.
In addition to the cold start model, statistics on input tend to shift over time. This means that even if a machine learning model is well trained, over time system performance can degrade because the input statistics shift.
It is within this context that the present embodiments arise.